宿州学院学报2026，Vol.41Issue(6)：36-41,84,7.DOI:10.3969/j.issn.1673-2006.2026.06.007
坚硬覆岩综放开采导水裂隙带高度机器学习预测研究
Machine Learning Prediction of Water-Flowing Fractured Zone Height for Fully Mechanized Caving Mining Under Hard Roof
摘要
Abstract
Accurately predicting the height of the water-flowing fractured zone(WFFZ)is of great significance for the rational design of waterproof coal pillars when mining under loose aquifers.To address the prediction of WFFZ height in fully mechanized caving mining under hard roof conditions,this study systematically collected 38 sets of field-measured data from 31 hard roof working faces nationwide and proposed a Voting ensemble machine learning prediction model tailored for small sample data.Four machine learning algorithms—k-Nearest Neighbors(KNN),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and XGBoost-were selected as base learners.Fea-ture engineering that incorporated empirical formulas as prior knowledge was developed;the Optuna framework was utilised for hyperparameter tuning;and a"soft voting"strategy was employed to construct the Voting ensemble mo-del.This approach effectively addresses the stability and accuracy issues inherent in prediction models dealing with small sample sizes and has been applied to on-site engineering practice.The research results indicate that the Voting ensemble model achieves high prediction precision,outperforming both the standard"Guide"formulas and single machine learning models,whilst also clarifying the model's error distribution range under varying degrees of geolo-gical complexity.Engineering applications demonstrate that the predicted values align well with field-measured data.These research findings provide a theoretical basis for the design of waterproof coal pillars in fully mechanized top-coal caving mining under hard roofs.关键词
综放开采/坚硬覆岩/导水裂隙带高度/小样本/机器学习Key words
fully mechanized caving mining/hard roof/height of water-flowing fractured zone/small-sample/ma-chine learning分类
矿业与冶金引用本文复制引用
袁同佟,鲁海峰..坚硬覆岩综放开采导水裂隙带高度机器学习预测研究[J].宿州学院学报,2026,41(6):36-41,84,7.基金项目
国家重点研发计划资助项目(2022YFF1303302). （2022YFF1303302）
